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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

Aiding Grasp Synthesis for Novel Objects Using Heuristic-Based and Data-Driven Active Vision Methods.

Sabhari Natarajan¹, Galen Brown², Berk Calli^1,2

¹Manipulation and Environmental Robotics Laboratory (MER Lab), Robotics Engineering Department, Worcester Polytechnic Institute, Worcester, MA, United States.

Frontiers in Robotics and AI

|August 2, 2021

Summary

This study introduces active vision strategies for robotic grasping, optimizing camera viewpoints to improve grasp synthesis. Heuristic methods demonstrated versatility and robustness in simulation and real-world tests.

Keywords:

active vision benchmarking grasp synthesis reinforcement learning self-supervised learning

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Robotic grasping requires efficient data collection for effective grasp synthesis.
Active vision strategies can optimize viewpoint selection for depth cameras.

Purpose of the Study:

To develop and evaluate heuristic-based and data-driven active vision strategies for viewpoint optimization.
To enhance the performance of robotic grasp synthesis algorithms.

Main Methods:

Created an open-source simulation benchmarking platform.
Proposed heuristic and data-driven active vision strategies.
Conducted experimental studies with a real-world gripper setup.

Main Results:

Quantitatively demonstrated the versatility of heuristic exploration methods.
Qualitatively showed robustness to novel objects and sim-to-real transfer.
Identified limitations and challenging scenarios for the proposed methods.

Conclusions:

Heuristic active vision strategies offer versatile and robust solutions for robotic grasping.
Further research is needed to address identified limitations and challenging scenarios.